[论文] Differences in Detection: Explainability Where it Matters

论文概要

研究领域: CV
作者: Johannes Theodoridis, Johannes Maucher, Andreas Schilling
发布时间: 2025-06-11
arXiv: 2506.08640

中文摘要

本文提出检测差异法(DnD)，一种直观比较两个目标检测模型的方法。基于相同的匹配算法，它补充了标准mAP指标和TIDE误差分析，能够直接比较两个模型。具体而言，我们计算两个模型均识别出的真实标签交集，以及对应的差异集和两个模型均漏检的真实标签补集。这种比较方式比独立汇总统计的对比更直接、更直观。它揭示了各自的错误和共同错误，当结合误差类型分析时尤为有效——检测误差的差异可在标准混淆矩阵中自然分析。

原文摘要

We propose Differences in Detection (DnD), an intuitive method to compare two object detection models. Based on the same matching algorithm, it complements the standard metrics of mean Average Precision (mAP) and TIDE error analysis with the ability to compare two models directly. More specifically, we calculate the intersection of ground truth labels that are recognized by both models, followed by the corresponding difference sets and the complement set of ground truth labels that are missed by both models. The resulting comparison is more direct and intuitive than a comparison of independent summary statistics. It reveals individual and shared mistakes and becomes particularly interesting when combined with error types. In this case, the differences in detection errors can be analyzed na...

自动采集于 2026-06-09

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